论文标题：A modified bootstrap for kernel-based specification test with heavy-tailed data

　　发表时间：2020

　　论文所有作者：Ta-Cheng Huang， Hongjun Li，Zheng Li

　　期刊名及所属分类：Economics Letters(国际B)

　　英文摘要：This paper provides a new resampling strategy to improve the finite sample performance of a nonparametric kernel-based specification test in the presence of heavy-tailed error terms. Based on the test statistic of Li and Wang (1998), we propose to generate the bootstrapped samples using a modified wild bootstrap. This new method matches all moments of the error terms if the error has a symmetric distribution and matches the first and all even moments when error distribution is asymmetric around zero. This new resampling method has better finite sample performance than the traditional one when the distribution of the error terms is symmetric and heavy-tailed.

　　中文摘要：本文提出了一种新的重采样策略，以改善存在重尾误差项的非参数kenel基规格测试的有限样本性能。基于Li和Wang(1998)的检验统计量，我们建议使用改进的野生bootstrap生成bootstrap样本。如果误差分布是对称的，则匹配误差项的所有矩;如果误差分布在0附近是不对称的，则匹配第一个和所有的偶数矩。在误差项分布为对称重尾的情况下，新的重采样方法比传统的重采样方法具有更好的有限采样性能。